The present application relates to methods for system identification and recursive adaptation. The methods described herein may be used to identify dynamic systems for use in model predictive control systems such as those sold by Johnson Controls, Inc.
The model is arguably the most important part of a control system. It is with the model that the controller predicts future system states and system outputs to determine optimal control actions. Without an accurate model, control actions may be suboptimal and possibly even unstable.
System identification is the process of determining a system of equations (e.g., a system model) that allow for the prediction of future system states or system outputs. System identification may be performed using “black-box” methods such as an ARMAX model or “gray-box” physics-based parameterizations of the system. In either case, the predicted output may be based the input-output history of the system.
Traditional system identification methods suffer from several disadvantages. First, traditional methods are based on the assumption of a linear model, which is often an inaccurate representation of the system. Even in the event that a system does behave linearly, there is still the problem of actuator saturation which occurs when a setpoint is stepped up by a large amount and the output begins to float. Additionally, traditional methods require long training periods of two days or more and may require additional training to adjust the model to a changing physical system.
A system identification method is needed which quickly and accurately identifies system parameters, which distinguishes between external disturbances to the system and changes to the system itself, and which adapts the model parameters to a changing physical system without needing to redevelop or retrain the model.